Difference between revisions of "RecSys 2019"
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|has program chair=Domonkos Tikk, Peter Brusilovsky | |has program chair=Domonkos Tikk, Peter Brusilovsky | ||
|Acronym =RecSys 2019 | |Acronym =RecSys 2019 | ||
| − | |End date =2019 | + | |End date =2019-09-20 |
|Series =RecSys | |Series =RecSys | ||
|Type =Conference | |Type =Conference | ||
| Line 13: | Line 13: | ||
|State =US/NY | |State =US/NY | ||
|City =US/NY/Copenhagen | |City =US/NY/Copenhagen | ||
| + | |Year =2019 | ||
|Homepage =https://recsys.acm.org/recsys19/ | |Homepage =https://recsys.acm.org/recsys19/ | ||
| − | |Start date =2019 | + | |Start date =2019-09-16 |
|Title =13th ACM Conference on Recommender Systems | |Title =13th ACM Conference on Recommender Systems | ||
|Accepted papers =76 | |Accepted papers =76 | ||
| − | |Submitted papers =354}} | + | |Submitted papers =354 |
| + | }} | ||
Topics of interest for RecSys 2019 include but are not limited to (alphabetically ordered | Topics of interest for RecSys 2019 include but are not limited to (alphabetically ordered | ||
Latest revision as of 03:14, 6 December 2021
Event Rating
| median | worst |
|---|---|
List of all ratings can be found at RecSys 2019/rating
| RecSys 2019 | |
|---|---|
13th ACM Conference on Recommender Systems
| |
| Event in series | RecSys |
| Dates | 2019-09-16 (iCal) - 2019-09-20 |
| Homepage: | https://recsys.acm.org/recsys19/ |
| Location | |
| Location: | US/NY/Copenhagen, US/NY, US |
| Important dates | |
| Abstracts: | 2019/04/15 |
| Papers: | 2019/04/23 |
| Submissions: | 2019/04/23 |
| Camera ready due: | 2019/07/22 |
| Papers: | Submitted 354 / Accepted 76 (21.5 %) |
| Committees | |
| General chairs: | Toine Bogers, Alain Said |
| PC chairs: | Domonkos Tikk, Peter Brusilovsky |
| Table of Contents | |
Topics of interest for RecSys 2019 include but are not limited to (alphabetically ordered
- Algorithm scalability, performance, and implementations
- Bias, bubbles and ethics of recommender systems
- Case studies of real-world implementations
- Context-aware recommender systems
- Conversational recommender systems
- Cross-domain recommendation
- Economic models and consequences of recommender systems
- Evaluation metrics and studies
- Explanations and evidence
- Innovative/New applications
- Interfaces for recommender systems
- Novel machine learning approaches to recommendation algorithms (deep learning, reinforcement learning, etc.)
- Preference elicitation
- Privacy and Security
- Social recommenders
- User modelling
- Voice, VR, and other novel interaction paradigms